Future Tech Jobs
Future Tech Jobs
This role involves analyzing end-to-end performance of large-scale AI workloads across compute, network, storage, and software stacks. The engineer will design performance studies, identify bottlenecks, and drive optimizations in collaboration with deep learning and platform teams. Work focuses on profiling, benchmarking, and delivering actionable insights to improve the efficiency and reliability of NVIDIA's DGX Cloud AI systems.
Design and build end-to-end applications that integrate AI-driven physics models into real-world engineering workflows. Collaborate with customers and technical teams to prototype, deploy, and iterate tools that solve complex engineering challenges across industries like aerospace, energy, and semiconductors. Work across the full stack with a focus on backend services, cloud infrastructure, and customer-driven product development.
Design and develop novel Transformer and GNN-based foundation models that generalize across relational databases and heterogeneous graphs. Work across the full ML lifecycle—from architecture design to inference optimization—on real-world applications like fraud detection, forecasting, and recommendation systems. Collaborate with researchers and engineers to build a unified AI model for enterprise data.
A Forward Deployed Software Engineer at PhysicsX works directly with customers and engineering teams to build and deploy AI-driven simulation applications that solve complex engineering problems across industries like aerospace, energy, and semiconductors. The role involves full-stack development, rapid prototyping, and on-site collaboration, with a focus on delivering production-grade tools using physics AI models. Engineers contribute to backend services, APIs, and platform features while shaping product direction based on real-world customer needs.
Lead the technical direction of complex AI and machine learning projects, from strategy through to client deployment, while mentoring engineering teams and shaping reusable solutions across the organisation. Design scalable, reliable systems using Python and cloud platforms, and act as a technical advisor to both internal teams and external clients in high-stakes, regulated environments. Drive innovation by evaluating and integrating new technologies while ensuring architectural soundness and delivery excellence.
This role involves designing, training, and deploying computer vision models across the full machine learning lifecycle. You will work closely with engineers, data scientists, and domain experts to build production-ready systems and evaluate model performance in complex real-world environments.
This role involves designing, building, and deploying advanced machine learning models to solve complex operational challenges in real-world environments. You will work closely with a high-calibre founding team, industrial data, and customer environments to take machine learning systems from early validation through to scalable deployment.
Lead the design and deployment of scalable, production-grade machine learning systems for high-impact clients in national security and AI safety. Collaborate across engineering, data science, and commercial teams to operationalise AI solutions, while mentoring junior engineers and shaping technical standards. Work on-site with government clients and contribute to ethical, secure AI practices in a cross-functional, client-facing role.
Design and deploy production-grade computer vision systems using deep learning techniques such as CNNs and Vision Transformers. Translate AI research into robust, real-world applications for defence and security environments. Work across the full development lifecycle, from research to deployment on cloud and edge platforms.
Develop an engineering-grade spiking neural network engine for machine vision applications in defence and security contexts. Translate advanced research into robust production software using C++ and machine vision techniques. Collaborate with researchers and engineers on SNNs, CNNs, and Transformers, with integration into operational systems.
Design and build scalable AI data pipelines and cloud-native services, productionising machine learning and NLP models into reliable systems. Collaborate with data scientists and engineers to deliver intelligent products, while mentoring junior staff and shaping engineering standards. Work with cutting-edge technologies including LLMs, Kubernetes, and cloud platforms in a hybrid environment.
Develop and deploy computer vision and neural network-based solutions for novel cybersecurity applications within a research-driven environment. Work on algorithm design using Python and contribute to simulation products in a senior capacity. The role involves collaboration with diverse clients and offers technical challenges in emerging domains.
Design and optimize machine learning models for hardware acceleration on platforms such as GPUs and QPUs, focusing on low-latency distributed systems and algorithm development. Work closely with international clients using cutting-edge simulation and modeling technologies. Operate as a senior individual contributor within an established engineering team.
This role involves leading a team of machine learning engineers while maintaining hands-on technical contributions to ML system design, deployment, and continuous improvement. The position shapes engineering best practices, mentors junior staff, and leads cross-functional teams through the full lifecycle of AI/ML projects—from discovery to production. It also includes supporting pre-sales efforts and ensuring robust, scalable solutions are delivered with clear communication to non-technical stakeholders.
Design and build scalable data systems for multi-modal scientific data, enabling efficient analysis and machine learning in a high-performance research environment. Focus on data pipeline architecture, storage optimization, and seamless integration between wet lab and ML teams. Work across cloud-native infrastructure to support large-scale, GPU-intensive model training and rapid iteration.
